Empirical Security Platform vs RunSybil: Side-by-Side Comparison (2026)

Empirical Security Platform: ML-based platform that predicts vulnerability exploitation probability per environment. built by Empirical Security. Core capabilities include Global ML model trained on ~2M exploitation events to forecast attacker behavior, Local ML model trained on organization-specific asset and control data for environment-specific risk scoring, Hourly near-real time exploitation telemetry across 17,000+ CVEs..

RunSybil: AI-driven continuous attack surface assessment and validation platform. built by RunSybil. Core capabilities include Automated asset and endpoint discovery across cloud environments and third-party services, Internal and external application attack surface mapping, AI-powered automated vulnerability testing with context awareness..

Both serve the Exposure Management market but differ in approach, feature depth, and target audience.

Empirical Security Platform differentiates with Global ML model trained on ~2M exploitation events to forecast attacker behavior, Local ML model trained on organization-specific asset and control data for environment-specific risk scoring, Hourly near-real time exploitation telemetry across 17,000+ CVEs. RunSybil differentiates with Automated asset and endpoint discovery across cloud environments and third-party services, Internal and external application attack surface mapping, AI-powered automated vulnerability testing with context awareness.

Empirical Security Platform is developed by Empirical Security. RunSybil is developed by RunSybil. Vendor maturity, funding stage, and team size can be important factors when evaluating long-term viability and support quality.

Empirical Security Platform and RunSybil serve similar Exposure Management use cases: both are Exposure Management tools, both cover Vulnerability Prioritization, Vulnerability. Review the feature comparison above to determine which fits your requirements.